Architecture comparison

Linear vs Supabase

Supabase runs closer to the foundational model (Middleware / wrapper) than Linear (Off the LLM map (in-house non-LLM ML)). No shared foundational model — different bets.

Linear

DevTools

Proximity

Off the LLM map (in-house non-LLM ML)

Models

—

Cloud · datastore

GCPPostgreSQLRedisMongoDB

Compliance

—

Architecture

Linear’s CTO has described the production stack as running on Google Cloud with Kubernetes, using React and Node/TypeScript, with Postgres for primary storage, Redis for queuing, and MongoDB for caching scenarios. The only clearly documented AI-related capability in the provided sources is product-level “AI workflows”; no source here establishes a specific foundational LLM provider in the core production architecture, so model_links is empty.

Supabase

DevTools

Proximity

Middleware / wrapper

Models

middleware / wrappers

Cloud · datastore

AWSPostgreSQL (pgvector)

Compliance

SOC 2

Architecture

Postgres backend-as-a-service; AI via pgvector + partner embeddings.

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